GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction

Information Extraction (IE) systems are traditionally domain-specific, requiring costly adaptation that involves expert schema design, data annotation, and model training. While Large Language Models have shown promise in zero-shot IE, performance degrades significantly in unseen domains where label definitions differ. This paper introduces GuideX, a novel method that automatically defines domain-specific schemas, infers guidelines, and generates synthetically labeled instances, allowing for better out-of-domain generalization. Fine-tuning LLaMa 3.1 with GuideX sets a new state-of-the-art across seven zero-shot Named Entity Recognition benchmarks. Models trained with GuideX gain up to 7 F1 points over previous methods without human-labeled data, and nearly 3 F1 points higher when combined with it. Models trained on GuideX demonstrate enhanced comprehension of complex, domain-specific annotation schemas.